This article traces the evolution of text classification from bag-of-words methods to modern language models, using the recently released Jev AI model as a case study. Jev is positioned as a fast and cost-effective classifier that balances generality with efficiency, outperforming specialized models while remaining simpler than general-purpose LLMs. The author provides historical context on classification techniques to help readers understand the recent hype surrounding Jev.